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              <p class="caption" role="heading"><span class="caption-text">Getting Started</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../getting_started/installation.html">Installation</a></li>
<li class="toctree-l1"><a class="reference internal" href="../getting_started/jetpack.html">Torch-TensorRT in JetPack</a></li>
<li class="toctree-l1"><a class="reference internal" href="../getting_started/quick_start.html">Quick Start</a></li>
<li class="toctree-l1"><a class="reference internal" href="../getting_started/capture_and_replay.html">Introduction</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">User Guide</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../user_guide/torch_tensorrt_explained.html">Torch-TensorRT Explained</a></li>
<li class="toctree-l1"><a class="reference internal" href="../user_guide/dynamic_shapes.html">Dynamic shapes with Torch-TensorRT</a></li>
<li class="toctree-l1"><a class="reference internal" href="../user_guide/saving_models.html">Saving models compiled with Torch-TensorRT</a></li>
<li class="toctree-l1"><a class="reference internal" href="../user_guide/runtime.html">Deploying Torch-TensorRT Programs</a></li>
<li class="toctree-l1"><a class="reference internal" href="../user_guide/using_dla.html">DLA</a></li>
<li class="toctree-l1"><a class="reference internal" href="../user_guide/mixed_precision.html">Compile Mixed Precision models with Torch-TensorRT</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/torch_compile_advanced_usage.html">Torch Compile Advanced Usage</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/vgg16_ptq.html">Deploy Quantized Models using Torch-TensorRT</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/engine_caching_example.html">Engine Caching</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/engine_caching_bert_example.html">Engine Caching (BERT)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/refit_engine_example.html">Refitting Torch-TensorRT Programs with New Weights</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/serving_torch_tensorrt_with_triton.html">Serving a Torch-TensorRT model with Triton</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/torch_export_cudagraphs.html">Torch Export with Cudagraphs</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/converter_overloading.html">Overloading Torch-TensorRT Converters with Custom Converters</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/custom_kernel_plugins.html">Using Custom Kernels within TensorRT Engines with Torch-TensorRT</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/auto_generate_converters.html">Automatically Generate a Converter for a Custom Kernel</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/auto_generate_plugins.html">Automatically Generate a Plugin for a Custom Kernel</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/mutable_torchtrt_module_example.html">Mutable Torch TensorRT Module</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/weight_streaming_example.html">Weight Streaming</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/pre_allocated_output_example.html">Pre-allocated output buffer</a></li>
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<p class="caption" role="heading"><span class="caption-text">Dynamo Frontend</span></p>
<ul class="current">
<li class="toctree-l1 current"><a class="current reference internal" href="#">TensorRT Backend for <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code></a></li>
<li class="toctree-l1"><a class="reference internal" href="dynamo_export.html">Compiling Exported Programs with Torch-TensorRT</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">TorchScript Frontend</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../ts/creating_torchscript_module_in_python.html">Creating a TorchScript Module</a></li>
<li class="toctree-l1"><a class="reference internal" href="../ts/creating_torchscript_module_in_python.html#working-with-torchscript-in-python">Working with TorchScript in Python</a></li>
<li class="toctree-l1"><a class="reference internal" href="../ts/creating_torchscript_module_in_python.html#saving-torchscript-module-to-disk">Saving TorchScript Module to Disk</a></li>
<li class="toctree-l1"><a class="reference internal" href="../ts/getting_started_with_python_api.html">Using Torch-TensorRT in Python</a></li>
<li class="toctree-l1"><a class="reference internal" href="../ts/getting_started_with_cpp_api.html">Using Torch-TensorRT in  C++</a></li>
<li class="toctree-l1"><a class="reference internal" href="../ts/ptq.html">Post Training Quantization (PTQ)</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">FX Frontend</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../fx/getting_started_with_fx_path.html">Torch-TensorRT (FX Frontend) User Guide</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Model Zoo</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/torch_compile_resnet_example.html">Compiling ResNet with dynamic shapes using the <cite>torch.compile</cite> backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/torch_compile_transformers_example.html">Compiling BERT using the <cite>torch.compile</cite> backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/torch_compile_stable_diffusion.html">Compiling Stable Diffusion model using the <cite>torch.compile</cite> backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/compile_hf_models.html">Compiling LLM models from Huggingface</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/torch_compile_gpt2.html">Compiling GPT2 using the Torch-TensorRT <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> frontend</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/torch_export_sam2.html">Compiling SAM2 using the dynamo backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/torch_export_flux_dev.html">Compiling FLUX.1-dev model using the Torch-TensorRT dynamo backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/notebooks.html">Legacy notebooks</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Python API Documentation</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../py_api/torch_tensorrt.html">torch_tensorrt</a></li>
<li class="toctree-l1"><a class="reference internal" href="../py_api/dynamo.html">torch_tensorrt.dynamo</a></li>
<li class="toctree-l1"><a class="reference internal" href="../py_api/logging.html">torch_tensorrt.logging</a></li>
<li class="toctree-l1"><a class="reference internal" href="../py_api/fx.html">torch_tensorrt.fx</a></li>
<li class="toctree-l1"><a class="reference internal" href="../py_api/ts.html">torch_tensorrt.ts</a></li>
<li class="toctree-l1"><a class="reference internal" href="../py_api/ptq.html">torch_tensorrt.ts.ptq</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">C++ API Documentation</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../_cpp_api/torch_tensort_cpp.html">Torch-TensorRT C++ API</a></li>
<li class="toctree-l1"><a class="reference internal" href="../_cpp_api/namespace_torch_tensorrt.html">Namespace torch_tensorrt</a></li>
<li class="toctree-l1"><a class="reference internal" href="../_cpp_api/namespace_torch_tensorrt__logging.html">Namespace torch_tensorrt::logging</a></li>
<li class="toctree-l1"><a class="reference internal" href="../_cpp_api/namespace_torch_tensorrt__torchscript.html">Namespace torch_tensorrt::torchscript</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">CLI Documentation</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../cli/torchtrtc.html">torchtrtc</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Contributor Documentation</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../contributors/system_overview.html">System Overview</a></li>
<li class="toctree-l1"><a class="reference internal" href="../contributors/dynamo_converters.html">Writing Dynamo Converters</a></li>
<li class="toctree-l1"><a class="reference internal" href="../contributors/writing_dynamo_aten_lowering_passes.html">Writing Dynamo ATen Lowering Passes</a></li>
<li class="toctree-l1"><a class="reference internal" href="../contributors/ts_converters.html">Writing TorchScript Converters</a></li>
<li class="toctree-l1"><a class="reference internal" href="../contributors/useful_links.html">Useful Links for Torch-TensorRT Development</a></li>
<li class="toctree-l1"><a class="reference internal" href="../contributors/resource_management.html">Resource Management</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Indices</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../indices/supported_ops.html">Operators Supported</a></li>
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  <section id="tensorrt-backend-for-torch-compile">
<span id="torch-compile"></span><h1>TensorRT Backend for <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code><a class="headerlink" href="#tensorrt-backend-for-torch-compile" title="Permalink to this heading">¶</a></h1>
<span class="target" id="module-torch_tensorrt.dynamo"></span><p>This guide presents the Torch-TensorRT <cite>torch.compile</cite> backend: a deep learning compiler which uses TensorRT to accelerate JIT-style workflows across a wide variety of models.</p>
<section id="key-features">
<h2>Key Features<a class="headerlink" href="#key-features" title="Permalink to this heading">¶</a></h2>
<p>The primary goal of the Torch-TensorRT <cite>torch.compile</cite> backend is to enable Just-In-Time compilation workflows by combining the simplicity of <cite>torch.compile</cite> API with the performance of TensorRT. Invoking the <cite>torch.compile</cite> backend is as simple as importing the <cite>torch_tensorrt</cite> package and specifying the backend:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">torch_tensorrt</span>
<span class="o">...</span>
<span class="n">optimized_model</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">backend</span><span class="o">=</span><span class="s2">&quot;torch_tensorrt&quot;</span><span class="p">,</span> <span class="n">dynamic</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
</pre></div>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Many additional customization options are available to the user. These will be discussed in further depth in this guide.</p>
</div>
<p>The backend can handle a variety of challenging model structures and offers a simple-to-use interface for effective acceleration of models. Additionally, it has many customization options to ensure the compilation process is fitting to the specific use case.</p>
</section>
<section id="customizable-settings">
<h2>Customizable Settings<a class="headerlink" href="#customizable-settings" title="Permalink to this heading">¶</a></h2>
<dl class="py class">
<dt class="sig sig-object py" id="torch_tensorrt.dynamo.CompilationSettings">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.dynamo.</span></span><span class="sig-name descname"><span class="pre">CompilationSettings</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">enabled_precisions:</span> <span class="pre">~typing.Set[~torch_tensorrt._enums.dtype]</span> <span class="pre">=</span> <span class="pre">&lt;factory&gt;,</span> <span class="pre">workspace_size:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">0,</span> <span class="pre">min_block_size:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">5,</span> <span class="pre">torch_executed_ops:</span> <span class="pre">~typing.Collection[~typing.Union[~collections.abc.Callable[[...],</span> <span class="pre">~typing.Any],</span> <span class="pre">str]]</span> <span class="pre">=</span> <span class="pre">&lt;factory&gt;,</span> <span class="pre">pass_through_build_failures:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">max_aux_streams:</span> <span class="pre">~typing.Optional[int]</span> <span class="pre">=</span> <span class="pre">None,</span> <span class="pre">version_compatible:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">optimization_level:</span> <span class="pre">~typing.Optional[int]</span> <span class="pre">=</span> <span class="pre">None,</span> <span class="pre">use_python_runtime:</span> <span class="pre">~typing.Optional[bool]</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">truncate_double:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">use_fast_partitioner:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">True,</span> <span class="pre">enable_experimental_decompositions:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">device:</span> <span class="pre">~torch_tensorrt._Device.Device</span> <span class="pre">=</span> <span class="pre">&lt;factory&gt;,</span> <span class="pre">require_full_compilation:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">disable_tf32:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">assume_dynamic_shape_support:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">sparse_weights:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">engine_capability:</span> <span class="pre">~torch_tensorrt._enums.EngineCapability</span> <span class="pre">=</span> <span class="pre">&lt;factory&gt;,</span> <span class="pre">num_avg_timing_iters:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">1,</span> <span class="pre">dla_sram_size:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">1048576,</span> <span class="pre">dla_local_dram_size:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">1073741824,</span> <span class="pre">dla_global_dram_size:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">536870912,</span> <span class="pre">dryrun:</span> <span class="pre">~typing.Union[bool,</span> <span class="pre">str]</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">hardware_compatible:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">timing_cache_path:</span> <span class="pre">str</span> <span class="pre">=</span> <span class="pre">'/tmp/torch_tensorrt_engine_cache/timing_cache.bin',</span> <span class="pre">lazy_engine_init:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">cache_built_engines:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">reuse_cached_engines:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">use_explicit_typing:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">use_fp32_acc:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">refit_identical_engine_weights:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">strip_engine_weights:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">immutable_weights:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">True,</span> <span class="pre">enable_weight_streaming:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">enable_cross_compile_for_windows:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">tiling_optimization_level:</span> <span class="pre">str</span> <span class="pre">=</span> <span class="pre">'none',</span> <span class="pre">l2_limit_for_tiling:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">-1,</span> <span class="pre">use_distributed_mode_trace:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">offload_module_to_cpu:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/dynamo/_settings.html#CompilationSettings"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.dynamo.CompilationSettings" title="Permalink to this definition">¶</a></dt>
<dd><p>Compilation settings for Torch-TensorRT Dynamo Paths</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>enabled_precisions</strong> (<em>Set</em><em>[</em><em>dpython:type</em><em>]</em>) – Available kernel dtype precisions</p></li>
<li><p><strong>debug</strong> (<em>bool</em>) – Whether to print out verbose debugging information</p></li>
<li><p><strong>workspace_size</strong> (<em>python:int</em>) – Workspace TRT is allowed to use for the module (0 is default)</p></li>
<li><p><strong>min_block_size</strong> (<em>python:int</em>) – Minimum number of operators per TRT-Engine Block</p></li>
<li><p><strong>torch_executed_ops</strong> (<em>Collection</em><em>[</em><em>Target</em><em>]</em>) – Collection of operations to run in Torch, regardless of converter coverage</p></li>
<li><p><strong>pass_through_build_failures</strong> (<em>bool</em>) – Whether to fail on TRT engine build errors (True) or not (False)</p></li>
<li><p><strong>max_aux_streams</strong> (<em>Optional</em><em>[</em><em>python:int</em><em>]</em>) – Maximum number of allowed auxiliary TRT streams for each engine</p></li>
<li><p><strong>version_compatible</strong> (<em>bool</em>) – Provide version forward-compatibility for engine plan files</p></li>
<li><p><strong>optimization_level</strong> (<em>Optional</em><em>[</em><em>python:int</em><em>]</em>) – Builder optimization 0-5, higher levels imply longer build time,
searching for more optimization options. TRT defaults to 3</p></li>
<li><p><strong>use_python_runtime</strong> (<em>Optional</em><em>[</em><em>bool</em><em>]</em>) – Whether to strictly use Python runtime or C++ runtime. To auto-select a runtime
based on C++ dependency presence (preferentially choosing C++ runtime if available), leave the
argument as None</p></li>
<li><p><strong>truncate_double</strong> (<em>bool</em>) – Whether to truncate float64 TRT engine inputs or weights to float32</p></li>
<li><p><strong>use_fast_partitioner</strong> (<em>bool</em>) – Whether to use the fast or global graph partitioning system</p></li>
<li><p><strong>enable_experimental_decompositions</strong> (<em>bool</em>) – Whether to enable all core aten decompositions
or only a selected subset of them</p></li>
<li><p><strong>device</strong> (<a class="reference internal" href="../py_api/torch_tensorrt.html#torch_tensorrt.Device" title="torch_tensorrt.Device"><em>Device</em></a>) – GPU to compile the model on</p></li>
<li><p><strong>require_full_compilation</strong> (<em>bool</em>) – Whether to require the graph is fully compiled in TensorRT.
Only applicable for <cite>ir=”dynamo”</cite>; has no effect for <cite>torch.compile</cite> path</p></li>
<li><p><strong>assume_dynamic_shape_support</strong> (<em>bool</em>) – Setting this to true enables the converters work for both dynamic and static shapes. Default: False</p></li>
<li><p><strong>disable_tf32</strong> (<em>bool</em>) – Whether to disable TF32 computation for TRT layers</p></li>
<li><p><strong>sparse_weights</strong> (<em>bool</em>) – Whether to allow the builder to use sparse weights</p></li>
<li><p><strong>engine_capability</strong> (<em>trt.EngineCapability</em>) – Restrict kernel selection to safe gpu kernels or safe dla kernels</p></li>
<li><p><strong>num_avg_timing_iters</strong> (<em>python:int</em>) – Number of averaging timing iterations used to select kernels</p></li>
<li><p><strong>dla_sram_size</strong> (<em>python:int</em>) – Fast software managed RAM used by DLA to communicate within a layer.</p></li>
<li><p><strong>dla_local_dram_size</strong> (<em>python:int</em>) – Host RAM used by DLA to share intermediate tensor data across operations</p></li>
<li><p><strong>dla_global_dram_size</strong> (<em>python:int</em>) – Host RAM used by DLA to store weights and metadata for execution</p></li>
<li><p><strong>dryrun</strong> (<em>Union</em><em>[</em><em>bool</em><em>, </em><em>str</em><em>]</em>) – Toggle “Dryrun” mode, which runs everything through partitioning, short of conversion to
TRT Engines. Prints detailed logs of the graph structure and nature of partitioning. Optionally saves the
output to a file if a string path is specified</p></li>
<li><p><strong>hardware_compatible</strong> (<em>bool</em>) – Build the TensorRT engines compatible with GPU architectures other than that of the GPU on which the engine was built (currently works for NVIDIA Ampere and newer)</p></li>
<li><p><strong>timing_cache_path</strong> (<em>str</em>) – Path to the timing cache if it exists (or) where it will be saved after compilation</p></li>
<li><p><strong>cache_built_engines</strong> (<em>bool</em>) – Whether to save the compiled TRT engines to storage</p></li>
<li><p><strong>reuse_cached_engines</strong> (<em>bool</em>) – Whether to load the compiled TRT engines from storage</p></li>
<li><p><strong>use_strong_typing</strong> (<em>bool</em>) – This flag enables strong typing in TensorRT compilation which respects the precisions set in the Pytorch model. This is useful when users have mixed precision graphs.</p></li>
<li><p><strong>use_fp32_acc</strong> (<em>bool</em>) – This option inserts cast to FP32 nodes around matmul layers and TensorRT ensures the accumulation of matmul happens in FP32. Use this only when FP16 precision is configured in enabled_precisions.</p></li>
<li><p><strong>refit_identical_engine_weights</strong> (<em>bool</em>) – Whether to refit the engine with identical weights</p></li>
<li><p><strong>strip_engine_weights</strong> (<em>bool</em>) – Whether to strip the engine weights</p></li>
<li><p><strong>immutable_weights</strong> (<em>bool</em>) – Build non-refittable engines. This is useful for some layers that are not refittable. If this argument is set to true, <cite>strip_engine_weights</cite> and <cite>refit_identical_engine_weights</cite> will be ignored</p></li>
<li><p><strong>enable_weight_streaming</strong> (<em>bool</em>) – Enable weight streaming.</p></li>
<li><p><strong>enable_cross_compile_for_windows</strong> (<em>bool</em>) – By default this is False means TensorRT engines can only be executed on the same platform where they were built.
True will enable cross-platform compatibility which allows the engine to be built on Linux and run on Windows</p></li>
<li><p><strong>tiling_optimization_level</strong> (<em>str</em>) – The optimization level of tiling strategies. A higher level allows TensorRT to spend more time searching for better tiling strategy. We currently support [“none”, “fast”, “moderate”, “full”].</p></li>
<li><p><strong>l2_limit_for_tiling</strong> (<em>python:int</em>) – The target L2 cache usage limit (in bytes) for tiling optimization (default is -1 which means no limit).</p></li>
<li><p><strong>use_distributed_mode_trace</strong> (<em>bool</em>) – Using aot_autograd to trace the graph. This is enabled when DTensors or distributed tensors are present in distributed model</p></li>
</ul>
</dd>
</dl>
</dd></dl>

<section id="custom-setting-usage">
<h3>Custom Setting Usage<a class="headerlink" href="#custom-setting-usage" title="Permalink to this heading">¶</a></h3>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">torch_tensorrt</span>
<span class="o">...</span>
<span class="n">optimized_model</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">backend</span><span class="o">=</span><span class="s2">&quot;torch_tensorrt&quot;</span><span class="p">,</span> <span class="n">dynamic</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
                                <span class="n">options</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;truncate_long_and_double&quot;</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span>
                                         <span class="s2">&quot;enabled_precisions&quot;</span><span class="p">:</span> <span class="p">{</span><span class="n">torch</span><span class="o">.</span><span class="n">float</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">half</span><span class="p">},</span>
                                         <span class="s2">&quot;debug&quot;</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span>
                                         <span class="s2">&quot;min_block_size&quot;</span><span class="p">:</span> <span class="mi">2</span><span class="p">,</span>
                                         <span class="s2">&quot;torch_executed_ops&quot;</span><span class="p">:</span> <span class="p">{</span><span class="s2">&quot;torch.ops.aten.sub.Tensor&quot;</span><span class="p">},</span>
                                         <span class="s2">&quot;optimization_level&quot;</span><span class="p">:</span> <span class="mi">4</span><span class="p">,</span>
                                         <span class="s2">&quot;use_python_runtime&quot;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,})</span>
</pre></div>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Quantization/INT8 support is slated for a future release; currently, we support FP16 and FP32 precision layers.</p>
</div>
</section>
</section>
<section id="compilation">
<h2>Compilation<a class="headerlink" href="#compilation" title="Permalink to this heading">¶</a></h2>
<p>Compilation is triggered by passing inputs to the model, as so:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">torch_tensorrt</span>
<span class="o">...</span>
<span class="c1"># Causes model compilation to occur</span>
<span class="n">first_outputs</span> <span class="o">=</span> <span class="n">optimized_model</span><span class="p">(</span><span class="o">*</span><span class="n">inputs</span><span class="p">)</span>

<span class="c1"># Subsequent inference runs with the same, or similar inputs will not cause recompilation</span>
<span class="c1"># For a full discussion of this, see &quot;Recompilation Conditions&quot; below</span>
<span class="n">second_outputs</span> <span class="o">=</span> <span class="n">optimized_model</span><span class="p">(</span><span class="o">*</span><span class="n">inputs</span><span class="p">)</span>
</pre></div>
</div>
</section>
<section id="after-compilation">
<h2>After Compilation<a class="headerlink" href="#after-compilation" title="Permalink to this heading">¶</a></h2>
<p>The compilation object can be used for inference within the Python session, and will recompile according to the recompilation conditions detailed below. In addition to general inference, the compilation process can be a helpful tool in determining model performance, current operator coverage, and feasibility of serialization. Each of these points will be covered in detail below.</p>
<section id="model-performance">
<h3>Model Performance<a class="headerlink" href="#model-performance" title="Permalink to this heading">¶</a></h3>
<p>The optimized model returned from <cite>torch.compile</cite> is useful for model benchmarking since it can automatically handle changes in the compilation context, or differing inputs that could require recompilation. When benchmarking inputs of varying distributions, batch sizes, or other criteria, this can save time.</p>
</section>
<section id="operator-coverage">
<h3>Operator Coverage<a class="headerlink" href="#operator-coverage" title="Permalink to this heading">¶</a></h3>
<p>Compilation is also a useful tool in determining operator coverage for a particular model. For instance, the following compilation command will display the operator coverage for each graph, but will not compile the model - effectively providing a “dryrun” mechanism:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">torch_tensorrt</span>
<span class="o">...</span>
<span class="n">optimized_model</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">backend</span><span class="o">=</span><span class="s2">&quot;torch_tensorrt&quot;</span><span class="p">,</span> <span class="n">dynamic</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
                                <span class="n">options</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;debug&quot;</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span>
                                         <span class="s2">&quot;min_block_size&quot;</span><span class="p">:</span> <span class="nb">float</span><span class="p">(</span><span class="s2">&quot;inf&quot;</span><span class="p">),})</span>
</pre></div>
</div>
<p>If key operators for your model are unsupported, see <span class="xref std std-ref">dynamo_conversion</span> to contribute your own converters, or file an issue here: <a class="reference external" href="https://github.com/pytorch/TensorRT/issues">https://github.com/pytorch/TensorRT/issues</a>.</p>
</section>
<section id="feasibility-of-serialization">
<h3>Feasibility of Serialization<a class="headerlink" href="#feasibility-of-serialization" title="Permalink to this heading">¶</a></h3>
<p>Compilation can also be helpful in demonstrating graph breaks and the feasibility of serialization of a particular model. For instance, if a model has no graph breaks and compiles successfully with the Torch-TensorRT backend, then that model should be compilable and serializeable via the <cite>torch_tensorrt</cite> Dynamo IR, as discussed in <a class="reference internal" href="../user_guide/dynamic_shapes.html#dynamic-shapes"><span class="std std-ref">Dynamic shapes with Torch-TensorRT</span></a>. To determine the number of graph breaks in a model, the <cite>torch._dynamo.explain</cite> function is very useful:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">torch</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">torch_tensorrt</span>
<span class="o">...</span>
<span class="n">explanation</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">_dynamo</span><span class="o">.</span><span class="n">explain</span><span class="p">(</span><span class="n">model</span><span class="p">)(</span><span class="o">*</span><span class="n">inputs</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Graph breaks: </span><span class="si">{</span><span class="n">explanation</span><span class="o">.</span><span class="n">graph_break_count</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
<span class="n">optimized_model</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">backend</span><span class="o">=</span><span class="s2">&quot;torch_tensorrt&quot;</span><span class="p">,</span> <span class="n">dynamic</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">options</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;truncate_long_and_double&quot;</span><span class="p">:</span> <span class="kc">True</span><span class="p">})</span>
</pre></div>
</div>
</section>
</section>
<section id="dynamic-shape-support">
<h2>Dynamic Shape Support<a class="headerlink" href="#dynamic-shape-support" title="Permalink to this heading">¶</a></h2>
<p>The Torch-TensorRT <cite>torch.compile</cite> backend will currently require recompilation for each new batch size encountered, and it is preferred to use the <cite>dynamic=False</cite> argument when compiling with this backend. Full dynamic shape support is planned for a future release.</p>
</section>
<section id="recompilation-conditions">
<h2>Recompilation Conditions<a class="headerlink" href="#recompilation-conditions" title="Permalink to this heading">¶</a></h2>
<p>Once the model has been compiled, subsequent inference inputs with the same shape and data type, which traverse the graph in the same way will not require recompilation. Furthermore, each new recompilation will be cached for the duration of the Python session. For instance, if inputs of batch size 4 and 8 are provided to the model, causing two recompilations, no further recompilation would be necessary for future inputs with those batch sizes during inference within the same session. Support for engine cache serialization is planned for a future release.</p>
<p>Recompilation is generally triggered by one of two events: encountering inputs of different sizes or inputs which traverse the model code differently. The latter scenario can occur when the model code includes conditional logic, complex loops, or data-dependent-shapes. <cite>torch.compile</cite> handles guarding in both of these scenario and determines when recompilation is necessary.</p>
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